An unmanned ship robust optimization control method and system with input saturation constraint

Abstract

The embodiment of the application discloses a kind of unmanned ship robust optimization control method and system with input saturation limit, its method includes: S1, the dynamic model of unmanned ship considering uncertainty and disturbance is established and corresponding online disturbance observer;S2, based on backstepping method, the steady-state controller of unmanned ship is designed;And based on adaptive dynamic programming technique, optimization additional item is designed, and the steady-state controller is combined with optimization additional item to obtain composite control law;S3, based on composite control law, final control command is generated to drive unmanned ship.The application can cope with unpredictable complex disturbance in marine environment by disturbance observer, secondly, backstepping method is combined with adaptive dynamic programming, and by the structure design of "steady-state controller+optimization additional item", complex tracking optimization problem is converted into the stabilization problem of error system;The application designs the auxiliary compensation system considering input saturation problem to process.

Description

Technical Field

[0001] This invention relates to the field of heading and trajectory control technology, and in particular to a robust optimization control method and system for unmanned surface vessels with input saturation limits. Background Technology

[0002] In recent years, with the deepening of marine resource development and utilization, unmanned surface vessels (USVs), with their core characteristics of intelligence and autonomy, have played an increasingly important role in fields such as marine exploration, environmental monitoring, and security patrols. Compared with traditional manned vessels, USVs can adapt to long-term, high-intensity continuous operations, greatly expanding human capabilities in exploring the ocean. However, USV platforms are typically small in size, resulting in limited energy carrying capacity. Therefore, optimizing energy consumption during control has become a key technological bottleneck for extending their operational endurance and improving mission efficiency.

[0003] To address the optimization control problem of unmanned surface vessel (USV) systems, existing technologies mainly employ the following two types of methods: The first type is Model Predictive Control (MPC). This method achieves precise control over the dynamic performance of the system by predicting and continuously optimizing the system's behavior over a future period at each time step. However, the dynamic model of unmanned vessels is highly nonlinear and strongly coupled. Solving this optimization problem online is computationally intensive and places stringent demands on hardware computing power, making it difficult to meet the real-time requirements of unmanned vessel systems for control algorithms. The second category is the Adaptive Dynamic Programming (ADP) method. Traditional ADP methods typically employ a multi-network structure of "model network-evaluation network-execution network," enabling online adjustment of controller parameters to adapt to uncertainties within the system. However, when unmanned vessels encounter rapidly changing environmental disturbances such as wind, waves, and currents in real sea conditions, the parameter adjustment and learning speed of this multi-network structure is relatively slow, often failing to compensate for disturbances in a timely and accurate manner. This leads to decreased control accuracy and may even affect navigation stability.

[0004] In summary, existing technologies face a mutually constraining technical dilemma when applied to unmanned surface vessel (USV) trajectory tracking optimization control: pursuing high-precision optimization control (such as MPC) inevitably leads to a surge in computational load, making it difficult to meet real-time requirements; while traditional adaptive methods (such as multi-network ADP) can adapt to uncertainty, their convergence speed and disturbance rejection performance are insufficient to cope with the challenges of complex marine environments; and most of the aforementioned methods neglect the hard constraint of thruster input saturation, which carries the risk of control command failure or even system instability under extreme conditions. Therefore, there is an urgent need to develop a new method for USV optimization control that can simultaneously overcome the triple barriers of computational efficiency, environmental adaptability, and physical constraints. Summary of the Invention

[0005] Based on this, in order to address the shortcomings of existing technologies, a robust optimization control method and system for unmanned vessels with input saturation constraints is proposed.

[0006] To achieve the above design objectives, the technical solution of the present invention is as follows: A robust optimization control method for unmanned surface vessels with input saturation constraints, comprising: S1. Establish a dynamic model of the unmanned vessel considering uncertainties and disturbances, and design a corresponding online disturbance observer for the dynamic model. The online disturbance observer is used to estimate the corresponding lumped disturbance estimate in real time based on the control input and real-time state feedback signal of the unmanned vessel, and use it as the feedforward compensation signal of the unmanned vessel. S2. A steady-state controller for an unmanned surface vessel (USV) is designed based on the backstepping method. The steady-state controller is used to receive the desired trajectory and the real-time state of the USV. A composite control law is obtained by combining the steady-state controller with an optimized additional term designed through adaptive dynamic programming. S3. Generate the final control command based on the composite control law to drive the unmanned vessel.

[0007] This application also provides a robust optimization control system for unmanned surface vessels with input saturation constraints, including: The first model processing unit is used to establish a dynamic model of the unmanned vessel considering uncertainties and disturbances and to design a corresponding online disturbance observer for the dynamic model. The online disturbance observer is used to estimate the corresponding lumped disturbance estimate in real time based on the control input and real-time state feedback signal of the unmanned vessel, and use it as the feedforward compensation signal of the unmanned vessel. The second model processing unit is used to design a steady-state controller for the unmanned vessel based on the backstepping method. The steady-state controller is used to receive the desired trajectory and the real-time state of the unmanned vessel. The composite control law is obtained by combining the steady-state controller with the optimization additional terms designed through adaptive dynamic programming. The command execution unit is used to generate final control commands based on the composite control law to drive the unmanned vessel.

[0008] Implementing the embodiments of the present invention will have the following beneficial effects: (1) Strong robustness and good environmental adaptability: Through the specially designed disturbance observer, this invention can estimate and compensate for unpredictable complex disturbances such as wind, waves and currents in the marine environment in real time and accurately. This active disturbance resistance mechanism significantly enhances the robustness of the control system and ensures that the unmanned vessel can maintain high-precision trajectory tracking performance under changing and harsh sea conditions. (2) Low algorithm complexity and good real-time performance: This invention innovatively combines backstepping with adaptive dynamic programming. Through the structural design of "steady-state controller + optimization additional terms", the complex tracking optimization problem is transformed into the stabilization problem of the error system. Furthermore, a single-network strategy iterative algorithm is adopted. Compared with the traditional multi-network structure of "model network-evaluation network-execution network", it greatly reduces the amount of online computation and the number of parameters that need to be adjusted, reduces the algorithm complexity, improves the convergence speed and real-time performance, and is easier to implement in engineering. (3) Considering physical constraints, it has high practicality: This invention clearly considers the input saturation problem that actually exists in the unmanned ship propulsion and designs a special auxiliary compensation system for this purpose. This design can ensure that the output command of the controller is always within the effective working range of the propulsion, avoids the performance degradation or system instability caused by excessive control command, and ensures the stable operation and reliability of the control algorithm on the actual hardware platform. It has high engineering application value. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] in: Figure 1 This is a flowchart of the basic steps corresponding to the solution described in this invention; Figure 2 A comparison diagram of the expected trajectory and actual navigation trajectory of an unmanned vessel using the solution described in this invention; Figure 3 , Figure 4 , Figure 5 The figures show the tracking error curves of the unmanned vessel using the scheme described in this invention in three degrees of freedom: forward direction, lateral position, and heading angle. Figure 6The actual output torque curve of the unmanned vessel propulsion system using the solution described in this invention is shown. Figure 7 This is a block diagram of the optimized control algorithm structure formed based on the basic steps described in this invention. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0012] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. It is understood that the terms “first,” “second,” etc., as used herein may be used to describe various elements, but these elements are not limited by these terms. These terms are used only to distinguish one element from another. For example, a first element may be referred to as a second element without departing from the scope of this application, and similarly, a second element may be referred to as a first element. Both the first element and the second element are elements, but they are not the same element.

[0013] Based on the aforementioned design requirements, this embodiment proposes a robust optimization control method for unmanned surface vessels with input saturation constraints, such as... Figure 1 and Figure 7 As shown, the method includes the following steps: S1. Establish a dynamic model of the unmanned vessel considering uncertainties and disturbances, and design a corresponding online disturbance observer for the dynamic model. The online disturbance observer is used to estimate the corresponding lumped disturbance estimate in real time based on the control input and real-time state feedback signal of the unmanned vessel, and use it as the feedforward compensation signal of the unmanned vessel. S2. A steady-state controller for an unmanned surface vessel (USV) is designed based on the backstepping method. The steady-state controller is used to receive the desired trajectory and the real-time state of the USV. A composite control law is obtained by combining the steady-state controller with an optimized additional term designed through adaptive dynamic programming. S3. Generate the final control command based on the composite control law to drive the unmanned vessel.

[0014] In some specific embodiments, to cope with unknown time-varying external environmental disturbances such as wind, waves, and currents, the purpose of S1 is to provide an unmanned vessel control system with a solution that can actively respond to unknown disturbances (an online disturbance observer with active disturbance rejection function). This involves establishing a dynamic model of the unmanned vessel considering uncertainties and disturbances, and designing a corresponding online disturbance observer based on feedforward compensation. The online disturbance observer estimates the lumped disturbance value corresponding to the dynamic model in real time based on the unmanned vessel's control input and real-time state feedback signals, and uses this estimate as a feedforward compensation signal. This online disturbance observer does not rely on an accurate environmental model; it only relies on the aforementioned real-time state information of the unmanned vessel to estimate the system's lumped disturbance online and can inject the feedforward compensation signal into the unmanned vessel's control loop in advance, thereby actively offsetting the adverse effects of the lumped disturbance on the dynamic performance of the unmanned vessel system. This provides crucial disturbance information for the subsequent design of the control law. This design is the prerequisite and foundation for improving the robustness of the entire control system. Specifically, the specific steps of S1 include: S11. Based on the dynamic mechanism of the unmanned vessel, a nonlinear dynamic model of the unmanned vessel is established; to ensure the feasibility of the unmanned vessel control system design, the dynamic model is simplified to obtain a simplified unmanned vessel model. The mathematical expression for the dynamic model of the unmanned vessel is as follows:

[0015] In the formula, Represents the position and state vector of the unmanned surface vessel in the northeast coordinate system, where These represent the positions of the unmanned surface vessel in the north and east directions in the northeast coordinate system, respectively. Indicates the bow angle of a ship. Represents the velocity state vector of the unmanned surface vessel in the hull coordinate system, where Indicates the pitch speed of the unmanned vessel. Indicates the ship's sway speed, Indicates the bow roll rate of the ship; The function representing the control input constraints is as follows: (2) In the formula, τ Represents force and torque. Represents boundary constraint values. , and All are positive numbers. Represents an unknown bounded disturbance. , A vector representing all components that are positive constants; This represents the unknown part of the unmanned vessel control system; This represents the inertial mass matrix of the unmanned surface vessel, which includes hydrodynamic inertia. Represents the damping matrix of an unmanned surface vessel. The matrix representing centripetal force and Coriolis force; S12. Simplify the dynamic model of the unmanned vessel to obtain a simplified model of the unmanned vessel, the mathematical expression of which is: (3) In the formula

[0016] (4) In the formula, The transformation matrix from the northeast coordinate system to the ship's coordinate system is represented as follows: ; The differences between the simplified model of the unmanned vessel and the real model-dynamic model, as well as external environmental disturbances, are uniformly regarded as lumped disturbances.

[0017] S13. Design an online disturbance observer corresponding to the simplified model of the unmanned vessel to perform real-time estimation and compensation of the lumped disturbance; specifically, it includes the following steps: S131. Define the auxiliary parameter vector. Introduce an auxiliary parameter vector related to the system state and / or lumped disturbances of the unmanned vessel, with the corresponding expression being:

[0018] in, , The design matrix represents positive definiteness. The time derivative can be expressed as: ; S132, Based on auxiliary parameter vector The corresponding update law is derived, that is, based on Lyapunov stability theory, the auxiliary parameter vector is derived. The estimated value of the update law is expressed as: ; This update law ensures the auxiliary parameter vector... It can effectively make dynamic adjustments to track changes in system disturbances and uncertainties. Specifically, the online disturbance observer dynamically adjusts the auxiliary parameter vector according to the update law to output a real-time updated lumped disturbance estimate.

[0019] In some specific embodiments, S2, a steady-state controller for the unmanned surface vessel (USV) is designed based on the backstepping method. This steady-state controller receives the desired trajectory and the real-time state of the USV. Specifically, it introduces the backstepping design concept and combines it with coordinate transformation to systematically construct the tracking error dynamics to achieve dynamic decoupling and control target transformation, thereby achieving control system stability. The backstepping method is a systematic controller design method applicable to strictly feedback-type nonlinear systems. Its core lies in decomposing the high-order system into multiple subsystems, designing virtual control laws at each level, and constructing Lyapunov functions to ensure dynamic stability of the error at each level, ultimately achieving global asymptotic stability or uniformity of the overall control system. Simultaneously, virtual optimization additional terms are designed based on optimization control theory and adaptive dynamic programming algorithms, and then the steady-state controller is combined with the virtual optimization additional terms to obtain a composite control law. Specifically, in step S2, the backstepping method is used to perform stepwise stability design on the simplified model of the unmanned vessel. By transforming its state vector, a series of tracking error surfaces are constructed as core variables for the controller design. At the same time, an auxiliary dynamic compensation system is introduced to predict and handle actuator saturation. Specifically, the following steps are included: S21. Constructing an error dynamic system considering actuator saturation: To address actuator saturation constraints, an auxiliary dynamic compensation system is designed. The state vector of the unmanned vessel is transformed into coordinates, and the state vector of the auxiliary system is introduced to obtain a new composite tracking error dynamic system. This transforms the complex desired trajectory tracking problem into a simpler error adjustment problem. The auxiliary dynamic compensation system designed to address actuator saturation is used to solve the problem that the physical limitations (thrust limit, angle limit) of actuators (such as thrusters and servos) in conventional technologies can easily lead to input saturation, which in turn can cause response hysteresis or even system instability. S22. Based on the composite tracking error dynamic system and the backstepping method, a steady-state controller for the unmanned vessel is designed; and a composite control law is obtained by combining the steady-state controller and the virtual optimization additional terms designed through adaptive dynamic programming technology.

[0020] This step not only constructs the tracking error dynamics of the system through coordinate transformation; based on the tracking error dynamics, following the backstepping design process, a novel virtual control law is gradually derived. It also innovatively decomposes the control law into two parts: a "steady-state controller" to ensure basic stability and an "optimization add-on" to pursue optimal performance (virtual control law: driven by an adaptive rate, used to compensate for unknown parameters or disturbance estimation; optimization add-on introduces performance-oriented virtual optimization terms). This decomposition gives the control strategy both environmental adaptability and task optimization potential, constructing the corresponding Lyapunov function. Finally, through stability analysis, a steady-state controller that ensures global consistency and eventual boundedness of the system is derived. Furthermore, within this framework, to address the common actuator saturation problem in practical engineering, an auxiliary compensation system is specifically designed based on this error dynamics. This auxiliary compensation system has dual functions: on the one hand, it can monitor the trend of control commands approaching the saturation threshold, predict saturation risks in advance, and adjust the control commands—adjusting the virtual control law—to proactively avoid saturation, i.e., an active defense mechanism; on the other hand, when saturation occurs, it can output a compensation signal to accelerate the system's escape from saturation, i.e., a passive recovery mechanism, preventing deep saturation and ensuring the continuity and stability of control. Therefore, it can be said that this design breaks through the limitations of traditional anti-saturation methods that rely solely on ex-post corrections, and achieves adaptive intervention management of "prediction-intervention-recovery".

[0021] Preferably, S21, the specific steps for constructing an error dynamic system considering actuator saturation include: To address actuator saturation constraints, an auxiliary dynamic compensation system is designed. This system performs coordinate transformation on the unmanned surface vessel's state vector and introduces the state vector of the auxiliary system to obtain a new composite tracking error dynamic system. The corresponding expression is: By performing coordinate transformation on the state vector of the unmanned vessel, the corresponding tracking error surface is obtained:

[0022]

[0023] in, It is a virtual control law. It is the design signal for the auxiliary compensation system. η d The tracking error surface is a reference signal. Its function is to indirectly adjust the control command to prevent actuator input saturation by adjusting the tracking error surface. When saturation occurs, it can also provide a compensation signal to help the actuator quickly escape the influence of saturation. The derivative formula for the tracking error surface is: .

[0024] Preferably, step S22 involves designing a steady-state controller for the unmanned surface vessel based on a composite tracking error dynamic system and the backstepping method; and obtaining a composite control law by combining the steady-state controller with the optimized additional terms designed using adaptive dynamic programming techniques. First, construct the first Lyapunov function and calculate its time derivative; the formula corresponding to the first Lyapunov function is: ; right Taking the derivative, its time derivative is obtained as follows: ; In the formula, z 1. z 2 are the first tracking error vector (also known as the error surface, which includes two types of errors: unmanned vessel position error and unmanned vessel bow angle error) and the second tracking error vector, respectively. Secondly, the composite control law Decomposed into virtual control laws With Virtual Optimization Add-ons ,Right now If the control law is decomposed into a steady-state controller that guarantees basic stability and an optimization term that pursues optimal performance, then the expression for the corresponding Lyapunov function is: .

[0025] Preferably, the steps of deriving the steady-state controller and the specific form of the optimization additional terms by combining the backstepping method include: S221. By making the derivative of the Lyapunov function negative definite, the virtual control law of the steady-state controller is derived in reverse. The specific steps include: Design a virtual control law for a steady-state controller. as follows:

[0026] in, It is the design parameter matrix , , For a positive design parameter, the corresponding Lyapunov function expression is:

[0027] right Taking the derivative, its time derivative can be written in the following form.

[0028] Among them, auxiliary signal The corresponding expression is

[0029] Using a fuzzy logic system (FLS) as a function approximator To approximate it, its expression is:

[0030] in, This represents the minimum approximation error. , If it is a constant matrix, then the corresponding The estimate can be expressed as: ; Design a second Lyapunov function, with the following expression: ; And because ,and Since it is a constant matrix, we can obtain:

[0031] Then we get:

[0032] According to Young's inequality, we can obtain:

[0033] definition ,in We can obtain the following formula:

[0034] Based on the above derivation, the control law for the corresponding steady-state controller is designed, and its expression is:

[0035] Design the corresponding adaptive rate, its expression is:

[0036] in, It is a design parameter matrix. , , Positive design parameters; S222. Constructing the Optimization Control Problem: The trajectory tracking optimization control problem of the unmanned vessel is transformed into a stabilization optimization problem for the tracking error system. Taking the composite tracking error system obtained in the previous steps as the controlled object, the stabilization optimization problem of the composite tracking error system is solved based on a single-network adaptive dynamic programming framework to generate the optimization additional term. The network adaptive dynamic programming framework essentially refers to designing a single-network policy iterative algorithm. This algorithm uses only a single evaluation network structure and utilizes a fuzzy logic system as a function approximator of the evaluation network to approximate the optimal cost function of the Hamilton-Jacobi-Bellman equation, and obtains the optimal control policy, i.e., the optimization additional term, through iterative solution.

[0037] The steps for reverse-engineering the specific form of the optimization addendum include: S2221. Based on the tracking error surface, combined with the virtual optimization additional item. Its corresponding The following error system is obtained:

[0038] in, , , , ; S2222, The aforementioned steps are designed , , Substitute Solving for the problem and using Young's inequality for scaling, we obtain:

[0039] in, To be based on the tracking error surface Combined with optimization of additional items and The obtained error system; where, , , , , Analysis reveals that the first four terms in the formula are non-negative, and the fifth through eighth terms are all bounded. Therefore, it can be concluded that when an optimized control method is used to design the controller... track hour, It will become a negative value, at which point the tracking error... The unmanned vessel control system remains bounded and optimized. However, optimizing the control... Since it cannot be obtained directly, the optimization cost function needs to be approximated using the ADP algorithm to obtain the optimization control law of the error system. This optimization control law is the optimization additional term part of the unmanned surface vessel optimization controller. The corresponding steps include: Design an evaluation network based on FLS to approximate and optimize the cost function.

[0040] in It is the ideal value of the parameter. It is a fuzzy basis function. It is the minimum fuzzy approximation error. The estimate obtained through FLS approximation It can be represented as

[0041] in yes The estimated value is defined as the FLS coefficient estimation error. , ,right beg The partial derivatives of can be obtained.

[0042] in, and They are and The derivative of .

[0043] The optimized controller is represented as follows:

[0044] The Hamiltonian equation is expressed as follows:

[0045] It can be obtained

[0046] Optimization control obtained through evaluation network The estimated value, and its corresponding formula is:

[0047] Obtained through review websites The estimated value, and its corresponding formula is:

[0048] In the formula, , , The parameter update rate of the design evaluation network can then be expressed in the following form: .

[0049] This step is actually a process of solving the expression of the optimization addition term based on single-network adaptive dynamic programming. Specifically, it refers to: First, creatively transforming the trajectory tracking optimization control problem of unmanned vessels into a stabilization optimization problem concerning the tracking error system; Second, to solve this problem, this invention designs an efficient single-network policy iterative ADP algorithm. Compared with the traditional ADP method that relies on multiple networks (such as "model network-evaluation network-execution network") for complex iteration, this invention adopts a minimal structure containing only a single "critic network" and uses fuzzy logic system (FLS) to approximate the optimal cost function of the Hamilton-Jacobi-Bellman (HJB) equation, thereby fundamentally reducing the computational complexity of the algorithm and significantly improving the convergence speed, making high-performance optimization control possible on unmanned vessel platforms with limited computing resources.

[0050] In some specific embodiments, S3 is the step of generating a composite control law that takes into account input saturation, that is, superimposing the steady-state controller designed in step S2 with the calculated optimization additional terms to form the final composite control law. Based on the composite control law, the corresponding control command is generated and directly applied to the thruster of the unmanned vessel. Based on the same inventive concept, this invention also proposes a robust optimization control system for unmanned surface vessels with input saturation constraints, comprising: The first model processing unit is used to establish a dynamic model of the unmanned vessel considering uncertainties and disturbances and to design a corresponding online disturbance observer for the dynamic model. The online disturbance observer is used to estimate the corresponding lumped disturbance estimate in real time based on the control input and real-time state feedback signal of the unmanned vessel, and use it as the feedforward compensation signal of the unmanned vessel. The second model processing unit is used to design a steady-state controller for the unmanned vessel based on the backstepping method; and to obtain a composite control law by combining the steady-state controller with the optimization additional terms designed through adaptive dynamic programming technology. The command execution unit is used to generate final control commands based on the composite control law to drive the unmanned vessel.

[0051] Based on the same inventive concept, the present invention also proposes a computer-readable storage medium including computer instructions that, when executed on a computer, cause the computer to perform the method described thereon.

[0052] To verify the technical effectiveness of the method described in this invention, this embodiment uses an unmanned surface vessel (USV) as the controlled object and conducts a trajectory tracking experiment in a simulation environment containing time-varying external disturbances such as simulated wind, waves, and currents. A reasonable output saturation limit is set for the USV's thruster. The experimental results are attached. Figure 2 To be continued Figure 6 As shown.

[0053] Among them, the appendix Figure 2 The diagram shows a comparison between the desired and actual trajectories of the unmanned surface vessel (USV). As can be seen from the figure, the actual trajectory highly overlaps with the desired trajectory, demonstrating the excellent tracking and control accuracy of the method described in this invention. (Appendix) Figure 3 Appendix Figure 4 With appendix Figure 5 The tracking error curves of the unmanned surface vessel (USV) in the three degrees of freedom—forward direction, lateral position, and heading angle—are displayed. It can be seen that after the control method is activated, all errors are quickly suppressed and converge stably to near zero, indicating that the method of this invention possesses the characteristics of fast response and high stability. (Appendix) Figure 6 The actual output torque curve of the unmanned vessel's thruster is shown. As can be seen from the figure, throughout the entire control process, the thruster's output torque was consistently and strictly constrained within the preset saturation limit range, never exceeding the limit. This fully verifies that the auxiliary system designed in this invention can effectively handle input saturation problems, ensuring the physical feasibility and operational reliability of the control system.

[0054] The experimental results above demonstrate that the method described in this invention can achieve high-precision and high-stability trajectory tracking control of unmanned vessels under conditions of external disturbances and input saturation.

[0055] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A robust optimization control method for an unmanned surface vessel with thruster input saturation constraints, characterized in that, include: S1. Establish a dynamic model of the unmanned vessel considering uncertainties and disturbances, and design a corresponding online disturbance observer for the dynamic model. The online disturbance observer is used to estimate the corresponding lumped disturbance estimate in real time based on the control input and real-time state feedback signal of the unmanned vessel, and use it as the feedforward compensation signal of the unmanned vessel. S2. A steady-state controller for an unmanned surface vessel (USV) is designed based on the backstepping method. The steady-state controller is used to receive the desired trajectory and the real-time state of the USV. A composite control law is obtained by combining the steady-state controller with an optimization term designed through adaptive dynamic programming. S3. Generate the final control command based on the composite control law to drive the unmanned vessel; in, S11. Based on the dynamic mechanism of the unmanned vessel, a nonlinear dynamic model of the unmanned vessel is established; and the dynamic model is simplified to obtain a simplified model of the unmanned vessel. The mathematical expression for the dynamic model of the unmanned vessel is as follows: (1) In the formula, Represents the position and state vector of the unmanned surface vessel in the northeast coordinate system, where These represent the positions of the unmanned surface vessel in the north and east directions in the northeast coordinate system, respectively. Indicates the bow angle of a ship. Represents the velocity state vector of the unmanned surface vessel in the hull coordinate system, where Indicates the pitch speed of the unmanned vessel. Indicates the ship's sway speed, Indicates the bow roll rate of the ship; The function representing the control input constraints is as follows: (2) In the formula, τ Represents force and torque. Represents boundary constraint values. , and All are positive numbers. Represents an unknown bounded disturbance. , A vector representing all components that are positive constants; This represents the unknown part of the unmanned vessel control system; This represents the inertial mass matrix of the unmanned surface vessel, which includes hydrodynamic inertia. Represents the damping matrix of an unmanned surface vessel. The matrix representing centripetal force and Coriolis force; S12. Simplify the dynamic model of the unmanned vessel to obtain a simplified model of the unmanned vessel, the mathematical expression of which is: (3) In the formula (4)； In the formula, The transformation matrix from the northeast coordinate system to the ship's coordinate system is represented as follows: ； S13. Design an online disturbance observer corresponding to the simplified model of the unmanned vessel to perform real-time estimation and compensation of the lumped disturbance; specifically, it includes the following steps: S131. Define auxiliary parameter vector Introduce an auxiliary parameter vector related to the system state and lumped disturbances of the unmanned vessel, with the corresponding expression being: in, , The design matrix represents positive definiteness. The time derivative is expressed as: ； S132, Based on auxiliary parameter vector The corresponding update law is derived, that is, based on Lyapunov stability theory, the auxiliary parameter vector is derived. The estimated value of the update law is expressed as: ； Step S2 specifically includes the following steps: S21. Construct an error dynamic system considering actuator saturation: To cope with actuator saturation constraints, design an auxiliary dynamic compensation system, perform coordinate transformation on the state vector of the unmanned vessel, and introduce the state vector of the auxiliary system to obtain a new composite tracking error dynamic system. S22. Based on the composite tracking error dynamic system and the backstepping method, a steady-state controller for the unmanned vessel is designed; and a composite control law is obtained by combining the steady-state controller and the optimized additional terms designed through adaptive dynamic programming technology. Furthermore, The specific steps in S21, constructing an error dynamic system considering actuator saturation, include: To address actuator saturation constraints, an auxiliary dynamic compensation system is designed. This system performs coordinate transformation on the unmanned surface vessel's state vector and introduces the state vector of the auxiliary system to obtain a new composite tracking error dynamic system. The corresponding expression is: By performing coordinate transformation on the state vector of the unmanned vessel, the corresponding tracking error surface is obtained: in, It is a virtual control law. It is the design signal for the auxiliary compensation system. η d The reference signal has the following derivative formula: ; S22. Based on the composite tracking error dynamic system and the backstepping method, design a steady-state controller for the unmanned surface vessel; and combine the steady-state controller with the optimized additional terms designed through adaptive dynamic programming to obtain the composite control law: First, construct the first Lyapunov function and calculate its time derivative; the formula corresponding to the first Lyapunov function is: right Taking the derivative, we obtain its time derivative as follows: ； In the formula, z 1. z 2 are the first tracking error vector and the second tracking error vector, respectively; Secondly, the composite control law Decomposed into virtual adaptive laws With Virtual Optimization Add-ons ,Right now If the control law is decomposed into a steady-state controller that guarantees basic stability and an optimization term that pursues optimal performance, then the expression for the corresponding Lyapunov function is: ; Among them, the virtual control law is solved by combining the backstep method. With Virtual Optimization Add-ons The steps include: S221. By making the derivative of the Lyapunov function negative definite, the virtual control law of the steady-state controller is derived in reverse. The specific steps include: Design a virtual control law for a steady-state controller. as follows: in, It is the design parameter matrix , , For a positive design parameter, the corresponding Lyapunov function expression is: right The time derivative is given by the following form. Among them, auxiliary signal The corresponding expression is Using a fuzzy logic system (FLS) as a function approximator To approximate it, its expression is: in, This represents the minimum approximation error. , If it is a constant matrix, then the corresponding The estimate is expressed as: ； Design a second Lyapunov function with the following expression: ； And because ,and Since it is a constant matrix, we get: Then we get: According to Young's inequality, we have: definition ,in We get the following equation: Based on the above derivation, the control law for the corresponding steady-state controller is designed, and its expression is: Design the corresponding adaptive rate, its expression is: in, It is a design parameter matrix. , , Positive design parameters; S222. Constructing the optimization control problem: The trajectory tracking optimization control problem of the unmanned vessel is transformed into a stabilization optimization problem concerning the tracking error system. Taking the composite tracking error system obtained in the preceding steps as the controlled object, the stabilization optimization problem of the composite tracking error system is solved based on a single-network adaptive dynamic programming framework to generate the optimization additional terms. The specific steps include: S2221. Based on the tracking error surface, combined with the virtual optimization additional item. Its corresponding The following error system is obtained: in, , , , ; S2222, The aforementioned steps are designed , , Substitute Solving the problem and using Young's inequality for scaling, we obtain: in, Based on the first / second tracking error surface , Combined with optimization of additional items and The obtained error system; where, , , , , The steps to obtain the optimized additional items include: Design an evaluation network based on FLS to approximate and optimize the cost function. in It is the ideal value of the parameter. It is a fuzzy basis function. It is the minimum fuzzy approximation error; the estimate obtained through FLS approximation. Represented as in yes The estimated value is defined as the FLS coefficient estimation error. , ,right beg The partial derivatives are obtained. in, and They are and The derivative; The optimized controller is represented as follows: The Hamiltonian equation is expressed as follows: get Optimization control obtained through evaluation network The estimated value, and its corresponding formula is: Obtained through review websites The estimated value is given by the formula: In the formula, , , The parameter update rate of the design evaluation network can then be expressed in the following form: 。 2. A robust optimization control system for an unmanned surface vessel (USV) designed according to the robust optimization control method for thruster input saturation limiting as described in claim 1, characterized in that, include: The first model processing unit is used to establish a dynamic model of the unmanned vessel considering uncertainties and disturbances and to design a corresponding online disturbance observer for the dynamic model. The online disturbance observer is used to estimate the corresponding lumped disturbance estimate in real time based on the control input and real-time state feedback signal of the unmanned vessel, and use it as the feedforward compensation signal of the unmanned vessel. The second model processing unit is used to design a steady-state controller for the unmanned vessel based on the backstepping method. The steady-state controller is used to receive the desired trajectory and the real-time state of the unmanned vessel. The composite control law is obtained by combining the steady-state controller with the optimization additional terms designed through adaptive dynamic programming. The command execution unit is used to generate final control commands based on the composite control law to drive the unmanned vessel.

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